1. Field of the Invention
The invention relates generally to a video on demand (VoD) systems, and more particularly, to a system and method for streaming of video over grid network.
2. Discussion of the Background
The advent of digitization has brought a major shift in paradigm in the context of compression, storage and communication of video data. This has resulted in diversifying usage of digital video across a wide spectrum of application domains. With Internet playing a pivotal role in communication, access to video information has become ubiquitous. Today's scenarios require, conferences to be held and its proceedings to be shared in the form of digitized video across various sites in near-real time, without significant loss of quality. A trivial solution to the above would probably be to distribute the video data through direct download.
However, such a setup has latency overhead as access to the information content may only start once the entire data is downloaded. This leads to the evolution of the concept of streaming, which decomposes a media data into a stream of packets that could be transmitted efficiently over computer networks and on receipt at the client site, may be played without waiting for the complete set of media data. Different video streaming mechanisms take advantage of specialized network protocols like RTSP etc. Multimedia streaming finds its practical implementation in Video on Demand (VoD) systems. These systems incorporate video streaming with complementary technologies enabling subscribers to select videos from a catalog and watch them in near-real-time playback quality unlike the traditional TV broadcast services. Further interactivity like Fast forward, Rewind, Random seek, Pause etc. could also be introduced at the subscriber location to facilitate virtual video playback.
However, such an efficient VoD implementation requires significant computation power, adequate storage and abundant network bandwidth. Recently, research in the high performance community has led to the development of the Grid Computing technologies, where heterogeneous computing and networking infrastructure combine to form a single computing infrastructure. Grid computing provides resource on demand and hence provides a high Return on Investment (ROI), as the resources can be shared as per need.
The various techniques that perform compression on video data encode these data at certain rates (bit-rate). The higher the bit-rate, the better is the quality of the video and the higher the data size. Large data put more constraints on the network bandwidth during transfer. Therefore delivery of video through streaming is largely determined by the availability of network bandwidth. Let us assume a video data has been encoded at a bitrate of μ kilobits/sec. This data can be played over a network without significant loss of quality, provided the network may furnish consistent bandwidth β, over a period of time t, is βt≧μt+p where p is the transmission payload. Best-effort packet networks like the Internet may not provide such consistent bandwidths and hence video may be re-encoded at a rate lesser than the available bandwidth and subsequently transmitted.
In an effort to solve this problem, many VoD systems implement Real-time stream bit rate switching or adaptive streaming, which adjust streaming according to the availability of network bandwidth. Such an order of adaptation to network bandwidth may be achieved either by (a) Video is stored as data pre-encoded in multiple bit-rates and transmitted, subjected to the availability of bandwidth or (b) Video is encoded dynamically according to the changes of bandwidth. Strategy (a) would require a total storage space S=σni μi for each video data, where ni is the number of instances the data is encoded and μi being the different pre-encoding bit-rates.
For a Video on Demand system that needs to cater to N different video requests, the total storage space is at least NS storage units. Since these requests are random, a highly sophisticated and scalable storage system needs to be deployed. Strategy (b) on the other hand involves encoding fragments of a video data at different bit-rates according to the fluctuations in transmission bandwidth and sending them in near-real time. As the number of requests surges, so does the network traffic at the VoD server which drastically strangles the delivery performance unless a highly robust and scalable network backbone is used. Encoding video itself is a compute intensive process and for every switch to a new bit-rate, the system would need to have computing power in the order of several gigaflops/sec. Also efficient management of factors like interrupt load and physical memory is necessary. Setting up hardware that resolves aforementioned issues incur huge installation and maintenance costs.
Accordingly, there is a need for a technique that integrates Grid with the Video-on-Demand systems through the development of Grid Based VoD (referred as GDVoD) system and overcomes the above mentioned limitations of the existing systems.